rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation
  name:
    name part: Bee Dagum, Estela.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Bianconcini, Silvia.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 283 p. 52 illus., 10 illus. in color. online resource.
  abstract: This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature. Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action. This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.
  table of contents: Introduction -- Time Series Components -- Part I: Seasonal Adjustment Methods -- Seasonal Adjustment: Meaning, Purpose and Methods -- Linear Filters Seasonal Adjustment Methods: Census Method II and its Variants -- Seasonal Adjustment Based on ARIMA Decomposition: TRAMO-SEATS.- Seasonal Adjustment Based on Structural Time Series Models -- Part II: Trend-Cycle Estimation.- Trend-Cycle Estimation.- Further Developments on the Henderson Trend-Cycle Filter.- A Unified View of Trend-Cycle Predictors in Reproducing Kernel Hilbert Spaces (RKHS).- Real Time Trend-Cycle Prediction.- The Effect of Seasonal Adjustment on Real-Time Trend-Cycle Prediction -- Glossary.
  note: by Estela Bee Dagum, Silvia Bianconcini.
  subject:
    topic: Statistics
  subject:
    topic: Probabilities
  subject:
    topic: Econometrics
  subject:
    topic: Macroeconomics
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
  subject:
    topic: Macroeconomics/Monetary Economics//Financial Economics
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Econometrics
  classification: QA276-280
  classification: 330.015195
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Statistics for Social and Behavioral Sciences
  identifier: 9783319318226
  identifier: http://dx.doi.org/10.1007/978-3-319-31822-6
  record info:
    record creation date: 160620
    record change date: 20180105143030.0
    record identifier: 978-3-319-31822-6